OpenRaiser/Intern-Atlas huggingface dataset:
Characterizing player's playing styles based on Player Vectors for each playing position in the Chinese Football Super League
Venue: arXiv cs.LG 2022
Year: 2022

Characterizing playing style is important for football clubs on scouting, monitoring and match preparation. Previous studies considered a player's style as a combination of technical performances, failing to consider the spatial information. Therefore, this study aimed to characterize the playing styles of each playing position in the Chinese Football Super League (CSL) matches, integrating a recently adopted Player Vectors framework. Data of 960 matches from 2016-2019 CSL were used. Match ratings, and ten types of match events with the corresponding coordinates for all the lineup players whose on-pitch time exceeded 45 minutes were extracted. Players were first clustered into 8 positions. A player vector was constructed for each player in each match based on the Player Vectors using Nonnegative Matrix Factorization (NMF). Another NMF process was run on the player vectors to extract different types of playing styles. The resulting player vectors discovered 18 different playing styles in the CSL. Six performance indicators of each style were investigated to observe their contributions. In general, the playing styles of forwards and midfielders are in line with football performance evolution trends, while the styles of defenders should be reconsidered. Multifunctional playing styles were also found in high rated CSL players.
A Structured Analysis of Journalistic Evaluations for News Source Reliability
Venue: arXiv cs.CY 2022
Year: 2022

In today's era of information disorder, many organizations are moving to verify the veracity of news published on the web and social media. In particular, some agencies are exploring the world of online media and, through a largely manual process, ranking the credibility and transparency of news sources around the world. In this paper, we evaluate two procedures for assessing the risk of online media exposing their readers to m/disinformation. The procedures have been dictated by NewsGuard and The Global Disinformation Index, two well-known organizations combating d/misinformation via practices of good journalism. Specifically, considering a fixed set of media outlets, we examine how many of them were rated equally by the two procedures, and which aspects led to disagreement in the assessment. The result of our analysis shows a good degree of agreement, which in our opinion has a double value: it fortifies the correctness of the procedures and lays the groundwork for their automation.
Beyond Diagonal Reconfigurable Intelligent Surfaces: From Transmitting and Reflecting Modes to Single-, Group-, and Fully-Connected Architectures
Venue: arXiv cs.IT 2022
Year: 2022

Reconfigurable intelligent surfaces (RISs) are envisioned as a promising technology for future wireless communications. With various hardware realizations, RISs can work under different modes (reflective/transmissive/hybrid) or have different architectures (single/group/fully-connected). However, most existing research focused on single-connected reflective RISs, mathematically characterized by diagonal phase shift matrices, while there is a lack of a comprehensive study for RISs unifying different modes/architectures. In this paper, we solve this issue by analyzing and proposing a general RIS-aided communication model. Specifically, we establish an RIS model not limited to diagonal phase shift matrices, a novel branch referred to as beyond diagonal RIS (BD-RIS), unifying modes and architectures. With the proposed model, we develop efficient algorithms to jointly design transmit precoder and BDRIS matrix to maximize the sum-rate for RIS-aided systems. We also provide simulation results to compare the performance of BD-RISs with different modes/architectures. Simulation results show that under the same mode, fully- and group-connected RIS can effectively increase the sum-rate performance compared with single-connected RIS, and that hybrid RIS outperforms reflective/transmissive RIS with the same architecture.
Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham
Venue: arXiv cs.LG 2022
Year: 2022

Automated disease detection in neuroimaging holds promise to improve the diagnostic ability of radiologists, but routinely collected clinical data frequently contains technical and demographic confounding factors that cause data to both differ between sites and be systematically associated with the disease of interest, thus negatively affecting the robustness of diagnostic models. There is a critical need for diagnostic deep learning models that can train on such imbalanced datasets without being influenced by these confounds. In this work, we introduce a novel deep learning architecture, MUCRAN (Multi-Confound Regression Adversarial Network), to train a deep learning model on clinical brain MRI while regressing demographic and technical confounding factors. We trained MUCRAN using 17,076 clinical T1 Axial brain MRIs collected from Massachusetts General Hospital before 2019 and demonstrated that MUCRAN could successfully regress major confounding factors in the vast clinical data. We also applied a method for quantifying uncertainty across an ensemble of these models to automatically exclude out-of-distribution data in the AD detection. By combining MUCRAN and the uncertainty quantification method, we showed consistent and significant increases in the AD detection accuracy for newly collected MGH data (post-2019) and for data from other hospitals. MUCRAN offers a generalizable approach for heterogenous clinical data for deep-learning-based automatic disease detection.
Evaluating Context for Deep Object Detectors
Venue: arXiv cs.CV 2022
Year: 2022

Which object detector is suitable for your context sensitive task? Deep object detectors exploit scene context for recognition differently. In this paper, we group object detectors into 3 categories in terms of context use: no context by cropping the input (RCNN), partial context by cropping the featuremap (two-stage methods) and full context without any cropping (single-stage methods). We systematically evaluate the effect of context for each deep detector category. We create a fully controlled dataset for varying context and investigate the context for deep detectors. We also evaluate gradually removing the background context and the foreground object on MS COCO. We demonstrate that single-stage and two-stage object detectors can and will use the context by virtue of their large receptive field. Thus, choosing the best object detector may depend on the application context.
Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads
Venue: arXiv cs.DC 2022
Year: 2022

The growing electricity demand of cloud and edge computing increases operational costs and will soon have a considerable impact on the environment. A possible countermeasure is equipping IT infrastructure directly with on-site renewable energy sources. Yet, particularly smaller data centers may not be able to use all generated power directly at all times, while feeding it into the public grid or energy storage is often not an option. To maximize the usage of renewable excess energy, we propose Cucumber, an admission control policy that accepts delay-tolerant workloads only if they can be computed within their deadlines without the use of grid energy. Using probabilistic forecasting of computational load, energy consumption, and energy production, Cucumber can be configured towards more optimistic or conservative admission. We evaluate our approach on two scenarios using real solar production forecasts for Berlin, Mexico City, and Cape Town in a simulation environment. For scenarios where excess energy was actually available, our results show that Cucumber's default configuration achieves acceptance rates close to the optimal case and causes 97.0% of accepted workloads to be powered using excess energy, while more conservative admission results in 18.5% reduced acceptance at almost zero grid power usage.
Detection of Propaganda Techniques in Visuo-Lingual Metaphor in Memes
Venue: arXiv cs.CV 2022
Year: 2022

The exponential rise of social media networks has allowed the production, distribution, and consumption of data at a phenomenal rate. Moreover, the social media revolution has brought a unique phenomenon to social media platforms called Internet memes. Internet memes are one of the most popular contents used on social media, and they can be in the form of images with a witty, catchy, or satirical text description. In this paper, we are dealing with propaganda that is often seen in Internet memes in recent times. Propaganda is communication, which frequently includes psychological and rhetorical techniques to manipulate or influence an audience to act or respond as the propagandist wants. To detect propaganda in Internet memes, we propose a multimodal deep learning fusion system that fuses the text and image feature representations and outperforms individual models based solely on either text or image modalities.
Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing
Venue: arXiv cs.RO 2022
Year: 2022

We present the results of our autonomous racing virtual challenge, based on the newly-released Learn-to-Race (L2R) simulation framework, which seeks to encourage interdisciplinary research in autonomous driving and to help advance the state of the art on a realistic benchmark. Analogous to racing being used to test cutting-edge vehicles, we envision autonomous racing to serve as a particularly challenging proving ground for autonomous agents as: (i) they need to make sub-second, safety-critical decisions in a complex, fast-changing environment; and (ii) both perception and control must be robust to distribution shifts, novel road features, and unseen obstacles. Thus, the main goal of the challenge is to evaluate the joint safety, performance, and generalisation capabilities of reinforcement learning agents on multi-modal perception, through a two-stage process. In the first stage of the challenge, we evaluate an autonomous agent's ability to drive as fast as possible, while adhering to safety constraints. In the second stage, we additionally require the agent to adapt to an unseen racetrack through safe exploration. In this paper, we describe the new L2R Task 2.0 benchmark, with refined metrics and baseline approaches. We also provide an overview of deployment, evaluation, and rankings for the inaugural instance of the L2R Autonomous Racing Virtual Challenge (supported by Carnegie Mellon University, Arrival Ltd., AICrowd, Amazon Web Services, and Honda Research), which officially used the new L2R Task 2.0 benchmark and received over 20,100 views, 437 active participants, 46 teams, and 733 model submissions -- from 88+ unique institutions, in 58+ different countries. Finally, we release leaderboard results from the challenge and provide description of the two top-ranking approaches in cross-domain model transfer, across multiple sensor configurations and simulated races.
Privacy-from-Birth: Protecting Sensed Data from Malicious Sensors with VERSA
Venue: arXiv cs.CR 2022
Year: 2022

There are many well-known techniques to secure sensed data in IoT/CPS systems, e.g., by authenticating communication end-points, encrypting data before transmission, and obfuscating traffic patterns. Such techniques protect sensed data from external adversaries while assuming that the sensing device itself is secure. Meanwhile, both the scale and frequency of IoT-focused attacks are growing. This prompts a natural question: how to protect sensed data even if all software on the device is compromised? Ideally, in order to achieve this, sensed data must be protected from its genesis, i.e., from the time when a physical analog quantity is converted into its digital counterpart and becomes accessible to software. We refer to this property as PfB: Privacy-from-Birth. In this work, we formalize PfB and design Verified Remote Sensing Authorization (VERSA) -- a provably secure and formally verified architecture guaranteeing that only correct execution of expected and explicitly authorized software can access and manipulate sensing interfaces, specifically, General Purpose Input/Output (GPIO), which is the usual boundary between analog and digital worlds on IoT devices. This guarantee is obtained with minimal hardware support and holds even if all device software is compromised. VERSA ensures that malware can neither gain access to sensed data on the GPIO-mapped memory nor obtain any trace thereof. VERSA is formally verified and its open-sourced implementation targets resource-constrained IoT edge devices, commonly used for sensing. Experimental results show that PfB is both achievable and affordable for such devices.
Watching the watchers: bias and vulnerability in remote proctoring software
Venue: arXiv cs.CR 2022
Year: 2022

Educators are rapidly switching to remote proctoring and examination software for their testing needs, both due to the COVID-19 pandemic and the expanding virtualization of the education sector. State boards are increasingly utilizing these software for high stakes legal and medical licensing exams. Three key concerns arise with the use of these complex software: exam integrity, exam procedural fairness, and exam-taker security and privacy. We conduct the first technical analysis of each of these concerns through a case study of four primary proctoring suites used in U.S. law school and state attorney licensing exams. We reverse engineer these proctoring suites and find that despite promises of high-security, all their anti-cheating measures can be trivially bypassed and can pose significant user security risks. We evaluate current facial recognition classifiers alongside the classifier used by Examplify, the legal exam proctoring suite with the largest market share, to ascertain their accuracy and determine whether faces with certain skin tones are more readily flagged for cheating. Finally, we offer recommendations to improve the integrity and fairness of the remotely proctored exam experience.
A Highly Adaptive Acoustic Model for Accurate Multi-Dialect Speech Recognition
Venue: arXiv cs.LG 2022
Year: 2022

Despite the success of deep learning in speech recognition, multi-dialect speech recognition remains a difficult problem. Although dialect-specific acoustic models are known to perform well in general, they are not easy to maintain when dialect-specific data is scarce and the number of dialects for each language is large. Therefore, a single unified acoustic model (AM) that generalizes well for many dialects has been in demand. In this paper, we propose a novel acoustic modeling technique for accurate multi-dialect speech recognition with a single AM. Our proposed AM is dynamically adapted based on both dialect information and its internal representation, which results in a highly adaptive AM for handling multiple dialects simultaneously. We also propose a simple but effective training method to deal with unseen dialects. The experimental results on large scale speech datasets show that the proposed AM outperforms all the previous ones, reducing word error rates (WERs) by 8.11% relative compared to a single all-dialects AM and by 7.31% relative compared to dialect-specific AMs.
A Verifiable Multiparty Computation Solver for the Assignment Problem and Applications to Air Traffic Management
Venue: arXiv cs.CR 2022
Year: 2022

The assignment problem is an essential problem in many application fields and frequently used to optimize resource usage. The problem is well understood and various efficient algorithms exist to solve the problem. However, it was unclear what practical performance could be achieved for privacy preserving implementations based on multiparty computation (MPC) by leveraging more efficient solution strategies than MPC based simplex solvers for linear programs. We solve this question by implementing and comparing different optimized MPC algorithms to solve the assignment problem for reasonable problem sizes. Our empirical approach revealed various insights to MPC based optimization and we measured a significant (50x) speedup compared to the known simplex based approach. Furthermore, we also study the overhead introduced by making the results publicly verifiable by means of non-interactive zero-knowledge proofs. By leveraging modern proof systems we also achieve significant speedup for proof and verification times compared to the previously proposed approaches as well as compact proof sizes.
QLEVR: A Diagnostic Dataset for Quantificational Language and Elementary Visual Reasoning
Venue: arXiv cs.CV 2022
Year: 2022

Synthetic datasets have successfully been used to probe visual question-answering datasets for their reasoning abilities. CLEVR (johnson2017clevr), for example, tests a range of visual reasoning abilities. The questions in CLEVR focus on comparisons of shapes, colors, and sizes, numerical reasoning, and existence claims. This paper introduces a minimally biased, diagnostic visual question-answering dataset, QLEVR, that goes beyond existential and numerical quantification and focus on more complex quantifiers and their combinations, e.g., asking whether there are more than two red balls that are smaller than at least three blue balls in an image. We describe how the dataset was created and present a first evaluation of state-of-the-art visual question-answering models, showing that QLEVR presents a formidable challenge to our current models. Code and Dataset are available at https://github.com/zechenli03/QLEVR
How to Minimize the Weighted Sum AoI in Multi-Source Status Update Systems: OMA or NOMA?
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, the minimization of the weighted sum average age of information (AoI) in a multi-source status update communication system is studied. Multiple independent sources send update packets to a common destination node in a time-slotted manner under the limit of maximum retransmission rounds. Different multiple access schemes, i.e., orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are exploited here over a block-fading multiple access channel (MAC). Constrained Markov decision process (CMDP) problems are formulated to describe the AoI minimization problems considering both transmission schemes. The Lagrangian method is utilised to convert CMDP problems to unconstraint Markov decision process (MDP) problems and corresponding algorithms to derive the power allocation policies are obtained. On the other hand, for the case of unknown environments, two online reinforcement learning approaches considering both multiple access schemes are proposed to achieve near-optimal age performance. Numerical simulations validate the improvement of the proposed policy in terms of weighted sum AoI compared to the fixed power transmission policy, and illustrate that NOMA is more favorable in case of larger packet size.
From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data
Venue: arXiv cs.CV 2022
Year: 2022

Visual question answering (VQA) for remote sensing scene has great potential in intelligent human-computer interaction system. Although VQA in computer vision has been widely researched, VQA for remote sensing data (RSVQA) is still in its infancy. There are two characteristics that need to be specially considered for the RSVQA task. 1) No object annotations are available in RSVQA datasets, which makes it difficult for models to exploit informative region representation; 2) There are questions with clearly different difficulty levels for each image in the RSVQA task. Directly training a model with questions in a random order may confuse the model and limit the performance. To address these two problems, in this paper, a multi-level visual feature learning method is proposed to jointly extract language-guided holistic and regional image features. Besides, a self-paced curriculum learning (SPCL)-based VQA model is developed to train networks with samples in an easy-to-hard way. To be more specific, a language-guided SPCL method with a soft weighting strategy is explored in this work. The proposed model is evaluated on three public datasets, and extensive experimental results show that the proposed RSVQA framework can achieve promising performance.
Geodesics, Non-linearities and the Archive of Novelty Search
Venue: arXiv cs.LG 2022
Year: 2022

The Novelty Search (NS) algorithm was proposed more than a decade ago. However, the mechanisms behind its empirical success are still not well formalized/understood. This short note focuses on the effects of the archive on exploration. Experimental evidence from a few application domains suggests that archive-based NS performs in general better than when Novelty is solely computed with respect to the population. An argument that is often encountered in the literature is that the archive prevents exploration from backtracking or cycling, i.e. from revisiting previously encountered areas in the behavior space. We argue that this is not a complete or accurate explanation as backtracking - beside often being desirable - can actually be enabled by the archive. Through low-dimensional/analytical examples, we show that a key effect of the archive is that it counterbalances the exploration biases that result, among other factors, from the use of inadequate behavior metrics and the non-linearities of the behavior mapping. Our observations seem to hint that attributing a more active role to the archive in sampling can be beneficial.
Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data
Venue: arXiv cs.LG 2022
Year: 2022

Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against data privacy attacks. To the best of our knowledge, we are the first to directly compare the impact of differentially private training on two different neural network architectures, DenseNet121 and ResNet50. Extending the federated learning environments previously analyzed in terms of privacy, we simulated a heterogeneous and imbalanced federated setting by distributing images from the public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private baseline models achieved an area under the receiver operating characteristic curve (AUC) of $0.94$ on the binary classification task of detecting the presence of a medical finding. We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients. The attack was particularly successful during later training stages. To mitigate the risk of privacy breach, we integrated Rényi differential privacy with a Gaussian noise mechanism into local model training. We evaluate model performance and attack vulnerability for privacy budgets $ε\in$ {1, 3, 6, 10}. The DenseNet121 achieved the best utility-privacy trade-off with an AUC of $0.94$ for $ε$ = 6. Model performance deteriorated slightly for individual clients compared to the non-private baseline. The ResNet50 only reached an AUC of $0.76$ in the same privacy setting. Its performance was inferior to that of the DenseNet121 for all considered privacy constraints, suggesting that the DenseNet121 architecture is more robust to differentially private training.